Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study
Machine learning applications are becoming more ubiquitous in dairy farming decision support applications in areas such as feeding, animal husbandry, healthcare, animal behavior, milking and resource management. Thus, the objective of this mapping study was to collate and assess studies published in...
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| Format: | Article |
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MDPI AG
2021-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/1/52 |
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| author | Philip Shine Michael D. Murphy |
| author_facet | Philip Shine Michael D. Murphy |
| author_sort | Philip Shine |
| collection | DOAJ |
| description | Machine learning applications are becoming more ubiquitous in dairy farming decision support applications in areas such as feeding, animal husbandry, healthcare, animal behavior, milking and resource management. Thus, the objective of this mapping study was to collate and assess studies published in journals and conference proceedings between 1999 and 2021, which applied machine learning algorithms to dairy farming-related problems to identify trends in the geographical origins of data, as well as the algorithms, features and evaluation metrics and methods used. This mapping study was carried out in line with PRISMA guidelines, with six pre-defined research questions (RQ) and a broad and unbiased search strategy that explored five databases. In total, 129 publications passed the pre-defined selection criteria, from which relevant data required to answer each RQ were extracted and analyzed. This study found that Europe (43% of studies) produced the largest number of publications (RQ1), while the largest number of articles were published in the Computers and Electronics in Agriculture journal (21%) (RQ2). The largest number of studies addressed problems related to the physiology and health of dairy cows (32%) (RQ3), while the most frequently employed feature data were derived from sensors (48%) (RQ4). The largest number of studies employed tree-based algorithms (54%) (RQ5), while RMSE (56%) (regression) and accuracy (77%) (classification) were the most frequently employed metrics used, and hold-out cross-validation (39%) was the most frequently employed evaluation method (RQ6). Since 2018, there has been more than a sevenfold increase in the number of studies that focused on the physiology and health of dairy cows, compared to almost a threefold increase in the overall number of publications, suggesting an increased focus on this subdomain. In addition, a fivefold increase in the number of publications that employed neural network algorithms was identified since 2018, in comparison to a threefold increase in the use of both tree-based algorithms and statistical regression algorithms, suggesting an increasing utilization of neural network-based algorithms. |
| first_indexed | 2024-03-10T03:21:46Z |
| format | Article |
| id | doaj.art-14cc100487934998b3bfec7b6c0b1d0b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:21:46Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-14cc100487934998b3bfec7b6c0b1d0b2023-11-23T12:16:07ZengMDPI AGSensors1424-82202021-12-012215210.3390/s22010052Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping StudyPhilip Shine0Michael D. Murphy1Department of Process, Energy and Transport Engineering, Munster Technological University, T12 P928 Cork, IrelandDepartment of Process, Energy and Transport Engineering, Munster Technological University, T12 P928 Cork, IrelandMachine learning applications are becoming more ubiquitous in dairy farming decision support applications in areas such as feeding, animal husbandry, healthcare, animal behavior, milking and resource management. Thus, the objective of this mapping study was to collate and assess studies published in journals and conference proceedings between 1999 and 2021, which applied machine learning algorithms to dairy farming-related problems to identify trends in the geographical origins of data, as well as the algorithms, features and evaluation metrics and methods used. This mapping study was carried out in line with PRISMA guidelines, with six pre-defined research questions (RQ) and a broad and unbiased search strategy that explored five databases. In total, 129 publications passed the pre-defined selection criteria, from which relevant data required to answer each RQ were extracted and analyzed. This study found that Europe (43% of studies) produced the largest number of publications (RQ1), while the largest number of articles were published in the Computers and Electronics in Agriculture journal (21%) (RQ2). The largest number of studies addressed problems related to the physiology and health of dairy cows (32%) (RQ3), while the most frequently employed feature data were derived from sensors (48%) (RQ4). The largest number of studies employed tree-based algorithms (54%) (RQ5), while RMSE (56%) (regression) and accuracy (77%) (classification) were the most frequently employed metrics used, and hold-out cross-validation (39%) was the most frequently employed evaluation method (RQ6). Since 2018, there has been more than a sevenfold increase in the number of studies that focused on the physiology and health of dairy cows, compared to almost a threefold increase in the overall number of publications, suggesting an increased focus on this subdomain. In addition, a fivefold increase in the number of publications that employed neural network algorithms was identified since 2018, in comparison to a threefold increase in the use of both tree-based algorithms and statistical regression algorithms, suggesting an increasing utilization of neural network-based algorithms.https://www.mdpi.com/1424-8220/22/1/52dairymachine learningartificial intelligenceprecision agricultureprecision livestock farming |
| spellingShingle | Philip Shine Michael D. Murphy Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study Sensors dairy machine learning artificial intelligence precision agriculture precision livestock farming |
| title | Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study |
| title_full | Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study |
| title_fullStr | Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study |
| title_full_unstemmed | Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study |
| title_short | Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study |
| title_sort | over 20 years of machine learning applications on dairy farms a comprehensive mapping study |
| topic | dairy machine learning artificial intelligence precision agriculture precision livestock farming |
| url | https://www.mdpi.com/1424-8220/22/1/52 |
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